Abstract
In this paper, we propose a new unbiased stochastic derivative estimator in a framework that can handle discontinuous sample performances with structural parameters. This work extends the three most popular unbiased stochastic derivative estimators: (1) infinitesimal perturbation analysis (IPA), (2) the likelihood ratio (LR) method, and (3) the weak derivative method, to a setting where they did not previously apply. Examples in probability constraints, control charts, and financial derivatives demonstrate the broad applicability of the proposed framework. The new estimator preserves the singlerun efficiency of the classic IPA-LR estimators in applications, which is substantiated by numerical experiments.
| Original language | English |
|---|---|
| Pages (from-to) | 487-499 |
| Number of pages | 13 |
| Journal | Operations Research |
| Volume | 66 |
| Issue number | 2 |
| Early online date | 2 Feb 2018 |
| DOIs | |
| Publication status | Published - Mar 2018 |
Funding
Funding:This work was supported in part by the National Natural Science Foundation of China [Grants 71571048, 71720107003, 71690232, 71371015], by the National Science Foundation [Grants CMMI-0856256, CMMI-1362303, CMMI-1434419], by the Air Force Office of Scientific Research [Grant FA9550-15-10050], and by the Science and Technology Agency of Sichuan Province [Grant 2014GZX0002].
| Funders | Funder number |
|---|---|
| Air Force Office of Scientific Research | FA9550-15-10050 |
| National Natural Science Foundation of China | 71720107003, 71371015, 71571048, 71690232 |
| Department of Science and Technology of Sichuan Province | 2014GZX0002 |
| National Science Foundation | CMMI-1362303, CMMI-0856256, CMMI-1434419 |
| National Aerospace Science Foundation of China |
Keywords
- Discontinuous sample performance
- Likelihood ratio
- Perturbation analysis
- Simulation
- Stochastic derivative estimation
- Weak derivative